code: def repeated_launch(
      experiment: str,
      num_workers: int = 1,
      seed: tp.Optional[int] = None,
      cap_index: tp.Optional[int] = None,
      output: tp.Optional[tp.PathLike] = None,
      plot: tp.Union[bool, tp.PathLike] = False,
      imports: tp.Optional[tp.List[tp.PathLike]] = None,
      repetitions: int = 1,
  ) -> None:
      """Launch experiment with given names and selection module
      cap_index can be specified to provide a limited number of settings
      This repeats the plan several times and increments the seed.
      """
      # start by importing additional content
      if imports is not None:
          assert isinstance(imports, (tuple, list))
          for path in imports:
              core.import_additional_module(path)
      # then run multiple times
      csvpath = Path("default.csv")
      for k in range(repetitions):
          print(f"Starting repetition {k +1} / {repetitions}")
          csvpath = launch(
              experiment,
              num_workers=num_workers,
              cap_index=cap_index,
              output=output,
              seed=None if seed is None else seed + k,
          )
      # save plots if need be
      if plot:
          df = utils.Selector.read_csv(csvpath)
          if isinstance(plot, bool):
              plot = str(Path(csvpath).with_suffix("")) + "_plots"
          print(f"Saving plots into folder: {plot}")
          plotting.create_plots(df, output_folder=plot)
  
file_path: nevergrad/nevergrad/benchmark/__main__.py
imports: import argparse
  from pathlib import Path
  from concurrent import futures
  import nevergrad.common.typing
  from .None import utils
  from .None import core
  from .None import plotting
label_desc: Use image node_id 3 for calling a global function with example usage: repeated_launch(experiment, num_workers, seed, cap_index, output, plot, imports, repetitions) without return types